Long-term tracking with fast scale estimation and efficient re-detection

Abstract

In long-term tracking applications, occlusion and scale variation are common attributes which cause performance degradation. Existing solutions use heavy calculation to deal with these problems, without considering the real-time implementation. Therefore, the authors propose a novel long-term tracker with fast scale estimation and efficient re-detection scheme to maintain real-time speed and favourable accuracy. Specifically, the authors integrate a distance metric method into correlation filter-based tracker to realise fast translation calculation and scale estimation. In addition, the authors advocate a keypoint-matching based confidence indicator to verify the tracking result and activate the re-detection module when the occlusion happens. The authors test our approach on challenging sequences with scale variation and occlusion. Experiments demonstrate that our proposed tracker procures preferable effect than state-of-the-art methods in the aspect of both speed and accuracy.

Keywords

• image sequences
• object detection
• object tracking
• image matching
• long-term tracker
• performance degradation
• long-term tracking applications
• fast scale estimation
• scale variation
• re-detection module
• keypoint-matching based confidence indicator
• fast translation calculation
• correlation filter-based tracker
• distance metric method
• real-time speed
• efficient re-detection scheme